Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.
PLoS One. 2021 Mar 17;16(3):e0242777. doi: 10.1371/journal.pone.0242777. eCollection 2021.
The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.
自 2020 年初以来，Covid-19 疫情已在全球范围内蔓延。许多地区都受到了影响。美国南卡罗来纳州自 2020 年 3 月初开始出现病例，并于 2020 年 4 月初出现首个高峰。4 月 6 日实施封锁，但从 4 月 24 日开始放宽限制。对 NCHS（死亡）通过纽约时报 GitHub 存储库报告的每日病例和死亡数据进行了分析，并提出了数据建模方法。还对预测进行了考虑，并评估了无症状传播作为潜在未观察到的效应的作用。检查了两个不同的时间段，并提供了一步预测。结果表明，在任何与县级病例动态相关的模型中，社会经济劣势、无症状传播和空间混杂都是重要因素。
